Juan holds a BEng (Mechatronics) and finished his PhD in robotics and autonomous systems at the Queensland University of Technology (QUT), Australia in 2022.
His primary interests comprise autonomous Small Unmanned Aerial Vehicle (sUAV) decision-making, machine learning and computer vision for sUAV remote sensing, with a focus on hyperspectral and high-resolution image processing. Juan has worked for research projects in biosecurity, environment monitoring and time-critical applications such as land search and rescue to find lost people in collapsed buildings and bushlands.
– Autonomous Systems
– Reinforcement Learning
– Remote Sensing
– Search and Rescue
– Hyperspectral image processing
–  CSIRO Data61 PhD Scholarship – Commonwealth Scientific and Industrial Research Organisation (CSIRO)
–  CSIRO Data61 Top up Scholarship – Commonwealth Scientific and Industrial Research Organisation (CSIRO)
- J. Sandino, F. Vanegas, F. Gonzalez, and F. Maire, “Autonomous UAV navigation for active perception of targets in uncertain and cluttered environments,” in Aerospace Conference, Big Sky, MT, USA: IEEE, 2020, pp. 1–12. (https://eprints.qut.edu.au/200148/)
- J. Sandino, F. Gonzalez, K. Mengersen, and K. J. Gaston, “UAVs and machine learning revolutionising invasive grass and vegetation surveys in remote arid lands,” Sensors, vol. 18, no. 2, p. 605, 2018. doi: 10.3390/s18020605 (https://doi.org/10.3390/s18020605).
- J. Sandino, G. Pegg, F. Gonzalez, and G. Smith, “Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence,” Sensors, vol. 18, no. 4, p. 944, 2018. doi: 10.3390/s18040944 (https://doi.org/10.3390/s18040944).
Personal website: https://juansandino.com/
Google Scholar: https://scholar.google.com/citations?user=K6Vw3bYAAAAJ&hl=en
- Aerial Mapping of Forests Affected by Pathogens using UAVs, Hyperspectral Sensors and Artificial Intelligence: Myrtle Rust
- Autonomous UAV decision making under environment and target detection uncertainty
- Detection and mapping of exotic weeds using UAS and machine learning: Bitou Bush Case Study
- Developing pest risk models of Buffel Grass using Unmanned Aerial Systems and Statistical methods
- UAV Remote Sensing and AI for Environmental Monitoring in Antarctica and in Polar Regions